R for Data Science assessment
options(repos = "http://cran.us.r-project.org")
#import and load the required packages
if (!require(ggpubr)) {
install.packages("ggpubr")
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if (!require(ggplot2)) {
install.packages("ggplot2")
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library(ggplot2)
if (!require(plotly)) {
install.packages("plotly")
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## kurtosis, skewness## Loading required package: DT1. Reading the gapminder into a tibble
## New names:
## Rows: 2607 Columns: 20
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (2): Country Name, continent dbl (18): ...1, Year, Agriculture, value added (%
## of GDP), CO2 emissions (me...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`## # A tibble: 2,607 × 20
## ...1 Country…¹ Year Agric…² CO2 e…³ Domes…⁴ Elect…⁵ Energ…⁶ Expor…⁷ Ferti…⁸
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 Afghanis… 1962 NA 0.0738 21.3 NA NA 4.88 7.45
## 2 1 Afghanis… 1967 NA 0.124 9.92 NA NA 6.77 7.45
## 3 2 Afghanis… 1972 NA 0.131 18.9 NA NA 14.8 7.45
## 4 3 Afghanis… 1977 NA 0.183 13.8 NA NA 11.7 7.45
## 5 4 Afghanis… 1982 NA 0.166 NA NA NA NA 7.45
## 6 5 Afghanis… 1987 NA 0.276 NA NA NA NA 7.46
## 7 6 Afghanis… 1992 NA 0.101 NA NA NA NA 7.50
## 8 7 Afghanis… 1997 NA 0.0608 NA NA NA NA 7.64
## 9 8 Afghanis… 2002 38.5 0.0411 NA NA NA 32.4 7.27
## 10 9 Afghanis… 2007 30.6 0.0879 0.535 NA NA 17.8 6.44
## # … with 2,597 more rows, 10 more variables: `GDP growth (annual %)` <dbl>,
## # `Imports of goods and services (% of GDP)` <dbl>,
## # `Industry, value added (% of GDP)` <dbl>,
## # `Inflation, GDP deflator (annual %)` <dbl>,
## # `Life expectancy at birth, total (years)` <dbl>,
## # `Population density (people per sq. km of land area)` <dbl>,
## # `Services, etc., value added (% of GDP)` <dbl>, pop <dbl>, …2. Filtering and plotting the data
## # A tibble: 259 × 20
## ...1 Country…¹ Year Agric…² CO2 e…³ Domes…⁴ Elect…⁵ Energ…⁶ Expor…⁷ Ferti…⁸
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 Afghanis… 1962 NA 0.0738 21.3 NA NA 4.88 7.45
## 2 10 Albania 1962 NA 1.44 NA NA NA NA 6.28
## 3 20 Algeria 1962 NA 0.485 NA NA NA 19.8 7.61
## 4 30 American… 1962 NA NA NA NA NA NA NA
## 5 40 Andorra 1962 NA NA NA NA NA NA NA
## 6 50 Angola 1962 NA 0.216 NA NA NA NA 7.40
## 7 60 Antigua … 1962 NA 1.82 NA NA NA NA 4.34
## 8 70 Arab Wor… 1962 NA 0.761 18.2 NA NA NA 6.96
## 9 80 Argentina 1962 NA 2.52 17.3 NA NA 4.69 3.09
## 10 90 Armenia 1962 NA NA NA NA NA NA 4.43
## # … with 249 more rows, 10 more variables: `GDP growth (annual %)` <dbl>,
## # `Imports of goods and services (% of GDP)` <dbl>,
## # `Industry, value added (% of GDP)` <dbl>,
## # `Inflation, GDP deflator (annual %)` <dbl>,
## # `Life expectancy at birth, total (years)` <dbl>,
## # `Population density (people per sq. km of land area)` <dbl>,
## # `Services, etc., value added (% of GDP)` <dbl>, pop <dbl>, …gapminder_plot <- ggplot(gapminder_filtered, aes(x = gdpPercap,
y = `CO2 emissions (metric tons per capita)`)) +
geom_point()
ggsave("gapminderplot.png")## Saving 7 x 5 in image## Warning: Removed 151 rows containing missing values (`geom_point()`).## Warning: Removed 151 rows containing missing values (`geom_point()`).
3. Finding correlation
correlation <- cor(gapminder_filtered$`CO2 emissions (metric tons per capita)`,
gapminder_filtered$gdpPercap, use = "complete.obs")
p_value <- cor.test(gapminder_filtered$`CO2 emissions (metric tons per capita)`,
gapminder_filtered$gdpPercap)$p.value
correlation## [1] 0.9260817## [1] 1.128679e-464 On the unfiltered data, answer “In what year is the correlation between ‘CO2 emissions (metric tons per capita)’ and gdpPercap the strongest?” Filter the dataset to that year for the next step…
gapminder_filtered <- gapminder %>%
filter(!is.na(`CO2 emissions (metric tons per capita)`), !is.na(gdpPercap))
cor_by_year <- gapminder_filtered %>%
group_by(Year) %>%
summarise(correlation = cor(`CO2 emissions (metric tons per capita)`, gdpPercap, use = "complete.obs"))
cor_by_year## # A tibble: 10 × 2
## Year correlation
## <dbl> <dbl>
## 1 1962 0.926
## 2 1967 0.939
## 3 1972 0.843
## 4 1977 0.793
## 5 1982 0.817
## 6 1987 0.810
## 7 1992 0.809
## 8 1997 0.808
## 9 2002 0.801
## 10 2007 0.720strongest_year_row <- cor_by_year %>%
filter(correlation == max(correlation)) %>%
slice(1)
strongest_year <- strongest_year_row
strongest_year## # A tibble: 1 × 2
## Year correlation
## <dbl> <dbl>
## 1 1967 0.9395 Using plotly, create an interactive scatter plot comparing ‘CO2 emissions (metric tons per capita)’ and gdpPercap, where the point size is determined by pop (population) and the color is determined by the continent. You can easily convert any ggplot plot to a plotly plot using the ggplotly() command.
## # A tibble: 259 × 20
## ...1 Country…¹ Year Agric…² CO2 e…³ Domes…⁴ Elect…⁵ Energ…⁶ Expor…⁷ Ferti…⁸
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 Afghanis… 1967 NA 0.124 9.92 NA NA 6.77 7.45
## 2 11 Albania 1967 NA 1.36 NA NA NA NA 5.39
## 3 21 Algeria 1967 10.3 0.632 28.0 NA NA 23.4 7.67
## 4 31 American… 1967 NA NA NA NA NA NA NA
## 5 41 Andorra 1967 NA NA NA NA NA NA NA
## 6 51 Angola 1967 NA 0.167 NA NA NA NA 7.40
## 7 61 Antigua … 1967 NA 9.11 NA NA NA NA 4.04
## 8 71 Arab Wor… 1967 NA 1.33 31.3 NA NA NA 6.93
## 9 81 Argentina 1967 9.98 2.86 18.7 NA NA 7.50 3.05
## 10 91 Armenia 1967 NA NA NA NA NA NA 3.61
## # … with 249 more rows, 10 more variables: `GDP growth (annual %)` <dbl>,
## # `Imports of goods and services (% of GDP)` <dbl>,
## # `Industry, value added (% of GDP)` <dbl>,
## # `Inflation, GDP deflator (annual %)` <dbl>,
## # `Life expectancy at birth, total (years)` <dbl>,
## # `Population density (people per sq. km of land area)` <dbl>,
## # `Services, etc., value added (% of GDP)` <dbl>, pop <dbl>, …1
What is the relationship between continent and ‘Energy use (kg of oil equivalent per capita)’? (stats test needed)
We want to test if there is a statistically significant difference in the Energy use per continent. Thus, continent is the predictor variable, and energy use is the outcome variable.
To use a parametric test, we must ensure that three assumptions are met: Normality, equal variances, and independence.
Normality assumption: To check for normality, we use a qqplot.
# ggqqplot(data = gapminder, x = `Energy use (kg of oil equivalent per capita)`, facet.by = 'continent')
ggplot(gapminder, aes(x = `Energy use (kg of oil equivalent per capita)`)) +
geom_histogram(bins = 30) +
facet_wrap(~ continent, scales = "free") +
xlab("Energy use (kg of oil equivalent per capita)") +
ylab("Frequency")## Warning: Removed 1197 rows containing non-finite values (`stat_bin()`).
As we can see, the data is not normally distributed. Therefore, we use a Kruskal-Wallis test
kruskal.test(gapminder$`Energy use (kg of oil equivalent per capita)`, gapminder$continent, na.action = "na.omit")##
## Kruskal-Wallis rank sum test
##
## data: gapminder$`Energy use (kg of oil equivalent per capita)` and gapminder$continent
## Kruskal-Wallis chi-squared = 318.68, df = 4, p-value < 2.2e-16As we can see, the p value is less than 2.2e-16, which is less than 0.05, which means that the energy use varies significantly between at least two continents.
2
Is there a significant difference between Europe and Asia with respect to ‘Imports of goods and services (% of GDP)’ in the years after 1990? (stats test needed) ### Filtering the data
gapminder_years <- gapminder %>%
filter(Year > 1990) %>%
filter(continent == "Asia" | continent == "Europe")Visualizations
# Create box plots
box_plot <- ggplot(gapminder_years, aes(x = continent, y = `Imports of goods and services (% of GDP)`, fill = continent)) +
geom_boxplot() +
labs(x = "Continent", y = "Imports of goods and services (% of GDP)", fill = "Continent") +
ggtitle("Box Plots of GDP Imports by Continent")
ggplotly(box_plot)## Warning: Removed 12 rows containing non-finite values (`stat_boxplot()`).# Create density plots
density_plot <- ggplot(gapminder_years, aes(x = `Imports of goods and services (% of GDP)`, fill = continent)) +
geom_density(alpha = 0.5) +
labs(x = "Imports of goods and services (% of GDP)", fill = "Continent") +
ggtitle("Density Plots of GDP Imports by Continent")
ggplotly(density_plot)## Warning: Removed 12 rows containing non-finite values (`stat_density()`).Visually, the two continent’s import of goods and services are very close with overlapping peaks, although the variances appear to be different and there appears to be 4 outliers in Asia.
Plotting a qqplot
# Filter data for the years after 1990
data <- gapminder_years
# Plot Q-Q plot with facet by continent
ggplotly(ggqqplot(data = gapminder_years, x = "`Imports of goods and services (% of GDP)`", facet.by = "continent"))## Warning: Removed 12 rows containing non-finite values (`stat_qq()`).## Warning: Removed 12 rows containing non-finite values (`stat_qq_line()`).
## Removed 12 rows containing non-finite values (`stat_qq_line()`).For Asia, there are a few points with a high GDP above the diagonal line. As normality has been violated, it would not be appropriate to use a parametric test, so we use the non-parametric Mann-Whitney-Wilcoxon Test.
Perform Mann-Whitney-Wilcoxon Test
result <- wilcox.test(`Imports of goods and services (% of GDP)` ~ continent, data = gapminder_years)
print(result)##
## Wilcoxon rank sum test with continuity correction
##
## data: Imports of goods and services (% of GDP) by continent
## W = 5707, p-value = 0.7867
## alternative hypothesis: true location shift is not equal to 0As the p-value is greater than 0.05, we did not find a significant difference in ‘Imports of goods and services (% of GDP)’ between Europe and Asia
3 What is the country (or countries) that has the highest
'Population density (people per sq. km of land area)'
across all years? (i.e., which country has the highest average ranking
in this category across each time point in the dataset?)
gapminder_pd <- gapminder %>%
group_by(`Country Name`) %>%
summarize(`Mean population density` = mean(`Population density (people per sq. km of land area)`, , na.rm = TRUE)) %>%
arrange(desc(`Mean population density`)) %>%
slice_head(n = 5)
datatable(gapminder_pd)mean_pd_plot <- gapminder_pd %>%
ggplot(aes(x = `Country Name`, y = `Mean population density`)) +
ggtitle('Mean population density of the top 5 countries') +
geom_bar(stat = 'Identity')
ggplotly(mean_pd_plot)gapminder_dense <- gapminder_pd %>% slice(1)
colnames(gapminder_dense)[1] <- "Country with the most population density"
datatable(gapminder_dense)As seen from bar chart, Macao SAR, China bas the greatest population density across the years, with a mean population density of 14732.04 people per square km.
4 What country (or countries) has shown the greatest increase in
'Life expectancy at birth, total (years)' between 1962 and
2007?
# Get the top 5 countries with the greatest increase in life expectancies
gapminder_difference <-gapminder %>%
filter(Year %in% c(1962, 2007)) %>%
group_by(`Country Name`)%>%
arrange((`Life expectancy at birth, total (years)`)) %>%
reframe(`Difference in Life expectancy (2007 - 1962)` = diff(`Life expectancy at birth, total (years)`)) %>%
arrange(desc(`Difference in Life expectancy (2007 - 1962)`)) %>%
slice(1:5)
gapminder_difference_plot <- gapminder_difference %>%
ggplot(aes(x = `Country Name`, y = `Difference in Life expectancy (2007 - 1962)`)) +
geom_bar(stat = "identity") +
labs(title = "Top 5 countries with the greatest increase in life expectancy from 1962 to 2007", y = 'Difference in Life expectancy in years for (2007 - 1962)')
ggplotly(gapminder_difference_plot)As seen from the above bar chart, the country whose life expectancy increased the most from 1962 - 2007 for Maldives